The overall aim of the proposed research is to develop a theoretical account of the processes underlying classification learning and performance. Particular attention will be paid to ill-defined concepts, where the component features have substantial but not perfect correlations with category membership. The proposed experiments sample a variety of stimuli, category structures and procedures in an attempt to provide a broad empirical base to evaluate alternative theories and to develop links between research with artificial concepts and performance with natural concepts. A related objective will be to further elaborate and test the predictions of a context theory of classification, which proposes that a substantial portion of classification performance is based not on category-level information but rather on the retrieval of specific item information. Consequently, many of the proposed experiments examine instance-category relationships to see how these two levels of information become integrated. By the end of the project period we aim to have a theory that accounts for performance across a range of situations and which will allow us to bring results derived from artificial concepts to bear on classification involving natural concepts and rules involving exceptions.